Text Only Domain Adaptation

Text-only domain adaptation in automatic speech recognition (ASR) focuses on improving ASR models' performance on new, unseen speech datasets using only textual transcriptions of that data, avoiding the need for costly and time-consuming audio recordings. Current research explores various approaches, including modifications to neural transducer architectures (like factorized neural transducers) and novel methods for aligning acoustic and textual representations, often employing techniques like down-sampling acoustic features or generating synthetic spectrograms from text. This research is significant because it offers a more efficient and scalable way to adapt ASR systems to diverse speech styles and languages, ultimately improving the accessibility and robustness of speech technologies.